Hands-on IndabaX Benin 2026 workshop on reliable AI agents, tool calling, tracing, and evals.
# Building Reliable AI Agents
**Tool Calling, Orchestration and Evaluation in Practice**
Deep Learning IndabaX Benin 2026 - Educational lab by BOSSA Chabel
In this 50-minute hands-on lab, you will build a small agent that chooses and
calls deterministic tools, take it from a fragile `5 / 8` baseline to `8 / 8`,
and add validation, controlled errors, tracing, and behavior-based evaluations.
> Making an agent work is easy. Knowing when it fails is harder.
## What you will learn
- How a tool-using agent differs from a chatbot.
- What an LLM function call contains and who executes it.
- How a bounded agent loop turns tool results into a final answer.
- Why generated arguments must be validated.
- How traces and a small eval suite make failures inspectable.
## Prerequisites
- Basic Python: functions, dictionaries, and JSON.
- Python 3.10+ for local execution, or Google Colab.
- A Gemini API key only if you want live mode. Mock mode is fully offline.
No Docker, database, GPU, vector store, or agent framework is required.
## Fastest start: Google Colab
1. Open the participant notebook.
2. Run the setup cell. It defaults to `MODE: MOCK` and needs no key.
3. Complete the three short TODOs.
4. If you are blocked, open the solution notebook.
## Local quick start
```bash
git clone
github.com
cd indabax-reliable-ai-agents
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
python -m evals.run_evals
```
Expected result: `8 / 8 passed`.
## Live Gemini mode
The default is deliberately offline and deterministic:
```bash
export LLM_MODE=mock
```
For a real function-calling run, create a key in
Google AI Studio, then inject it into your
shell without putting it in a file or notebook output:
```bash
export LLM_MODE=gemini
export GEMINI_API_KEY='your-key-here'
```
The selected mode is always printed. The workshop never silently presents mock
output as a live model response. Fr …